Turnuva seçim operatörü kullanan bir havai fişek algoritması

Öz

Son on yılda doğa olaylarından esinlenerek çeşitli sürü zekasına dayalı optimizasyon teknikleri geliştirilmiştir. Kabul edilebilir bir sürede optimuma yakın çözümler üretebilen bu teknikler, fen ve sosyal bilimlerdeki birçok problemin çözümünde başarıyla uygulanmıştır.  Havai Fişek Algoritması (HFA), havai fişeklerin patlamalarından esinlenilmiş yeni bir sürü zekası algoritmasıdır. Oldukça yeni sayılabilecek bu teknik, çok çeşitli problemlerde başarılı bir şekilde kullanılmış ve özellikle parçacık sürü optimizasyonu, karınca koloni ve genetik algoritma gibi tekniklere göre daha iyi sonuçlar elde edilmiştir. Elde edilen başarılı sonuçlara rağmen, HFA optimum çözüme ulaşmak için uzun zamana ihtiyaç duymaktadır. Bu hesaplama zamanı yetersizliğini giderebilmek amacıyla bu çalışmada turnuva seçimi kullanan bir HFA önerilmiştir. Turnuva seçme operatörüne sahip HFA’nın başarımı 15 adet nümerik optimizasyon probleminde test edilmiştir. Deneysel sonuçlar önerilen HFA’nın klasik HFA’ya göre hesaplama zamanı ve çözüm kalitesinde önemli performans iyileşmeleri sağladığını göstermiştir.

Anahtar Kelimeler

• Sürü zekası,Optimizasyon teknikleri,Nümerik optimizasyon

A fireworks algorithm using tournament selection operator

Abstract

In recent decade, several nature-inspired swarm intelligence-based optimization techniques have been improved. These techniques, which give solutions close to optimum in an acceptable time, have been applied successfully to solve the problems in science and social sciences. Fireworks Algorithm (FA), inspired by observing fireworks explosion, is a new swarm intelligence algorithm. This relatively new technique has been utilized to tackle diverse problems and obtained better performance than other popular techniques such as particle swarm optimization, ant colony, and genetic algorithm. Despite the good results obtained, FA requires long computation time to achieve the optimum solution. To eliminate long computation time drawback of FA, in this study, a FA using tournament selection is proposed. The performance of the proposed FA, which involves tournament selection operator, is tested on well-known 15 numerical optimization problems. Experimental results reveal that proposed FA has a significant performance improvement in term of computation time and solution quality in comparison with original FA.

Keywords

• Swarm intelligence,Optimization techniques,Numerical optimization

Kaynakça

1. Merkle D, Middendorf M. “Swarm intelligence and signal processing”. IEEE Signal Processing Magazine, 25(6), 152-158, 2008.
2. Karaboğa D. “Yapay Zeka Optimizasyon Algoritmaları”. 3. Baskı. Ankara, Türkiye, Nobel Akademik Yayıncılık, 2014.
3. Akdagli A, Guney K, Karaboga D, Babayigit B. “Finding failed element positions in linear antenna arrays using genetic algorithm”. 3rd International Conference on Electrical and Electronics Engineering, Bursa, Turkey, 3-7 December, 2003.
4. Chen Y, An A. “Application of ant colony algorithm to geochemical anomaly detection”. Journal of Geochemical Exploration, 164, 75-85, 2016.
5. Gao S, Wang Y, Cheng J, Inazumi Y, Tang Z. “Ant colony optimization with clustering for solving the dynamic location routing problem”. Applied Mathematics and Computation, 285, 149-173, 2016.
6. Kerdphol T, Fuji K, Mitani Y, Watanabe M, Qudaih Y. “Optimization of a battery energy storage system using particle swarm optimization for stand-alone microgrids”. International Journal of Electrical Power & Energy Systems, 81, 32-39, 2016.
7. Chuang LY, Moi SH, Lin Y-D, Yang CH. “A comparative analysis of chaotic particle swarm optimizations for detecting single nucleotide polymorphism barcodes”. Artificial Intelligence in Medicine, 73, 23-33, 2016.
8. Gong M, Yan J, Shen B, Ma L, Cai Q. “Influence maximization in social networks based on discrete particle swarm optimization”. Information Sciences, 367-368, 600-614, 2016.

9. Babayigit B, Akdagli A, Guney K. “A clonal selection algorithm for null synthesizing of linear antenna array by amplitude control”. Journal of Electromagnetic Waves and Applications, 20(8), 1007-1020, 2006.
10. Akdagli A, Guney K, Babayigit B. “Clonal selection algorithm for design of reconfigurable antenna array with discrete phase shifters”. Journal of Electromagnetic Waves and Applications, 21(2), 215-227, 2007.
11. Souza SSF, Romero R, Pereira J, Saraiva JT. “Artificial immune algorithm applied to distribution system reconfiguration with variable demand”. International Journal of Electrical Power & Energy Systems, 82, 561-568, 2016.
12. Tavana M, Kazemi MR, Vafadarnikjjoo A, Mobin M. “An artificial immune algorithm for ergonomic product classification using anthropometric measurements”. Measurement, 94, 621-629, 2016.
13. Hong PN, Ahn CW. “Linkage artificial bee colony for solving linkage problems”. Expert Systems with Applications, 61, 378-385, 2016.
14. Li B, Zhou C, Liu H, Li Y, Cao H. “Image retrieval via balance-evolution artificial bee colony algorithm and lateral inhibition”. Optik-International Journal for Light and Electron Optics, 127(24), 11775-11785, 2016.
15. Shah-Hosseini H. “The intelligent water drops algorithm: a nature-inspired swarm-based optimization algorithm”. International Journal of Bio-Inspired Computation, 1(1), 71-79, 2009.
16. Shi Y. “Brain storm optimization algorithm”. Advances in Swarm Intelligence, 6728, 303-309, 2011.
17. Tayarani NMH, Akbarzadeh TMR. “Magnetic optimization algorithms a new synthesis”. IEEE World Congress on Computational Intelligence Evolutionary Computation (CEC), Hong Kong, China, 1-6 June 2008.
18. Tan Y, Zhu A. “Fireworks algorithm for optimization”. Advances in Swarm Intelligence, 6145, 355-364, 2010.
19. Tan Y. Fireworks Algorithm A Novel Swarm Intelligence Optimization Method. 1st ed. New York, USA, Springer, 2015.
20. Tukey JW. Exploratory Data Analysis. Boston, MA, USA, Addison-Wesley, 1977.